# search.py
# ---------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html

"""
In search.py, you will implement generic search algorithms which are called
by Pacman agents (in searchAgents.py).
"""

import util

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples,
        (successor, action, stepCost), where 'successor' is a
        successor to the current state, 'action' is the action
        required to get there, and 'stepCost' is the incremental
        cost of expanding to that successor
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.  The sequence must
        be composed of legal moves
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other
    maze, the sequence of moves will be incorrect, so only use this for tinyMaze
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s,s,w,s,w,w,s,w]

def heapIn(successor,heap):
    for heapElement in heap:
        elMove = heapElement[1]
        if successor[0] == elMove[0]:
            return True
    return False

def customIn(successor,fringe):
    for triple in fringe:
        if successor[0] == triple[0]:
            return True
    return False

def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first
    [2nd Edition: p 75, 3rd Edition: p 87]

    Your search algorithm needs to return a list of actions that reaches
    the goal.  Make sure to implement a graph search algorithm
    [2nd Edition: Fig. 3.18, 3rd Edition: Fig 3.7].

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    print "Start:", problem.getStartState()
    print "Is the start a goal?", problem.isGoalState(problem.getStartState())
    print "Start's successors:", problem.getSuccessors(problem.getStartState())
    """
    "*** YOUR CODE HERE ***"
    from util import Stack
    from game import Directions
    print "Start:", problem.getStartState()
    print "Is the start a goal?", problem.isGoalState(problem.getStartState())
    print "Start's successors:", problem.getSuccessors(problem.getStartState())
    
    
    "a LIFO Queue with node as the only element"
    _stack = Stack()
    _explored = set()
    _nodePathDict = dict()
    
    if problem.isGoalState(problem.getStartState()):
        #print "FINISHED" 
        return [Directions.STOP]
    else:
        _explored.add(problem.getStartState())
        for _successor in problem.getSuccessors(problem.getStartState()):
            a = [_successor[1]]
            _nodePathDict[_successor[0]] = a
            _stack.push(_successor)
    
    #print "Dict",_nodePathDict
    #print "Stack",_stack.list
    while not _stack.isEmpty():
        "get possible move from top of stack, add its state to explored" 
        "_state is the current node being expanded"
        
        _state = _stack.pop()
        _explored.add(_state[0])
        
        for _successor in problem.getSuccessors(_state[0]):
            
            
            """
            parentPath = _nodePathDict[(_state[0])]
            parentPath.append(_successor[1])
            _nodePathDict[_successor[0]] = parentPath
            """
            
            
            a = []
            for pair in _nodePathDict[_state[0]]:
                a.append(pair)
            a.append(_successor[1])

            _nodePathDict[_successor[0]] = a
            if (not customIn(_successor,_stack.list)) and (_successor[0] not in _explored): 
                if problem.isGoalState(_successor[0]):
                    print _nodePathDict[_successor[0]]
                    return _nodePathDict[_successor[0]]
                _stack.push(_successor)

def breadthFirstSearch(problem):
    """
    Search the shallowest nodes in the search tree first.
    [2nd Edition: p 73, 3rd Edition: p 82]
    """
    "*** YOUR CODE HERE ***"
    from util import Queue
    from game import Directions
    
    _queue = Queue()
    _explored = set()
    _nodePathDict = dict()
    
    if problem.isGoalState(problem.getStartState()):
        #print "FINISHED" 
        return [Directions.STOP]
    else:
        _explored.add(problem.getStartState())
        for _successor in problem.getSuccessors(problem.getStartState()):
            a = [_successor[1]]
            _nodePathDict[_successor[0]] = a
            _queue.push(_successor)
    
    #print "Dict",_nodePathDict
    #print "Stack",_stack.list
    while not _queue.isEmpty():
        "get possible move from top of stack, add its state to explored" 
        "_state is the current node being expanded"
        
        _state = _queue.pop()
        #print "Parent:", _state[0],_nodePathDict[_state[0]]
        #print "    Successors:",problem.getSuccessors(_state[0])
        _explored.add(_state[0])
        
        for _successor in problem.getSuccessors(_state[0]):
            #print "        Sucessor:",_successor
            a = []
            for pair in _nodePathDict[_state[0]]:
                a.append(pair)
            a.append(_successor[1])
            
            #print "a",a
            _nodePathDict[_successor[0]] = a
            #print "            KVP:",_nodePathDict[_successor[0]]
            if (not customIn(_successor,_queue.list)) and (_successor[0] not in _explored): 
                if problem.isGoalState(_successor[0]):
                    return _nodePathDict[_successor[0]]
                _queue.push(_successor)
    

def uniformCostSearch(problem):
    "Search the node of least total cost first. "
    "*** YOUR CODE HERE ***"
    from util import PriorityQueue
    from game import Directions
    
    _queue = PriorityQueue()
    _explored = set()
    _nodePathDict = dict()
    
    
    
    
    if problem.isGoalState(problem.getStartState()):
        #print "FINISHED" 
        return [Directions.STOP]
    else:
        _explored.add(problem.getStartState())
        for _successor in problem.getSuccessors(problem.getStartState()):
            a = [_successor[1]]
            _nodePathDict[_successor[0]] = a
            _queue.push(_successor,_successor[2])
    
    #print "Dict",_nodePathDict
    #print "Stack",_stack.list
    while not _queue.isEmpty():
        "get possible move from top of stack, add its state to explored" 
        "_state is the current node being expanded"
        
        _state = _queue.pop()
        #print "Parent:", _state[0],_nodePathDict[_state[0]]
        #print "    Successors:",problem.getSuccessors(_state[0])
        if problem.isGoalState(_state[0]):
            return _nodePathDict[_state[0]]        
        _explored.add(_state[0])

        for _successor in problem.getSuccessors(_state[0]):
            #print "        Sucessor:",_successor
            if _successor[0] not in _explored:
                    _queue.push(_successor,_successor[2])
                    
            a = []
            for pair in _nodePathDict[_state[0]]:
                a.append(pair)
            a.append(_successor[1])
            _nodePathDict[_successor[0]] = a
            #print "            KVP:",_nodePathDict[_successor[0]]
                
def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0

def aStarSearch(problem, heuristic=nullHeuristic):
    "Search the node that has the lowest combined cost and heuristic first."
    "*** YOUR CODE HERE ***"
    """
    from util import PriorityQueue
    
    closedSet = set()
    openSet = PriorityQueue()
    _nodePathDict = dict()
    
    _costFromStartDict = dict()
    _heuristicCost = dict()
    _estimatedCost = dict()
    
    _costFromStartDict[problem.getStartState()] = 0
    _heuristicCost[problem.getStartState()] = heuristic(problem.getStartState(),problem)
    _estimatedCost = _costFromStartDict[problem.getStartState()] + _heuristicCost[problem.getStartState()]
    
    if problem.isGoalState(problem.getStartState()):
        return []
    closedSet.add(problem.getStartState())
    for _successor in problem.getSuccessors(problem.getStartState):
        if _successor[0] in closedSet:
            continue
        tentative_cost_from_start = _costFromStartDict[problem.getStartState]+_successor[2]
        
        
        if customIn(_successor[0],openSet.heap):
            openSet.push(item, priority)
    #initial start state evaluation
    
"""
# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
